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The main purpose of this study is to investigate the remodeling of risk management, risk-averse mechanism and the importance of Basel-III framework to cope with the current financial challenges in the regime of post global financial crises of 2008-2011 by evidences in the banking sectors of emerging economies of Bahrain, the UAE and Pakistan.

To ensure deep understanding in this cross-cultural study, two fold data collection techniques are used; one through distribution of questionnaires to relevant staff members and second through personal interviews of selected risk officials. Respondents are selected on the basis of minimum five years banking experience and relevant professional education of finance or risk management. Multistage sampling technique is used for data collection. To ensure the consistency from respondents, personal interviews were conducted with an interval of six months after receipt of questionnaires. Various statistical and econometric techniques were used to test the study hypotheses and to satisfy the study objectives.

Based on statistical analysis and personal surveys, research findings concluded that banking sectors of study-countries have deep concern with potential risk challenges and they are in continuous process to improve risk measurement framework in accordance with the latest regulatory obligations. All three types of banks have clear understanding of RM practices and strong relationship is observed between predictors and endogenous variables. Respondent banks of study-countries have deep attentiveness to manage all key risks and they recommend to transform existing regulatory framework including Basel-III reforms to develop a more comprehensive “one-size-fits-all” regulatory framework to cover loopholes of existing financial system.

This study is limited to the findings of remodeling of risk management to cope with the new financial challenges for the banking sector. Empirical investigation is conducted in emerging economies of the sub-continent and gulf and evidences are obtained from the UAE, Bahrain and Pakistan. Following this research model, future research can be extended to enlarge the sample size, by including other regional countries or a comparison between eastern and western countries to make it more useful to understand the risk management strategies, minimize banking default risks and to make this significant economic sector more strengthen.

Respondent countries of this study are fast growing and emerging economies of the sub-continent and gulf. Results of this cross-cultural study are likely to be beneficial for credit analysts, bankers and academic researchers. Findings are also beneficial for local and international business investors while they are taking prudent investment decisions in respective capital markets.

This is the first comparative study to empirically investigate the RM practices and risk-averse mechanism in banking sectors of Bahrain, the UAE and Pakistan. In perspective of study-countries, a critical analysis on risk-averse mechanism and Basel-III regulatory implications is demonstrated in this study.

This paper aims to answer how effective the loan-to-value (LTV) regulation has been since 2011 for conventional and Islamic (participation) banks in Turkey in terms of curbing mortgage loan growth and delinquency[1].

The authors first use unit root tests and tests of difference in loan and property price data in pre-LTV and post-LTV period. Second, the authors follow Chow test and ordinary least squares regression analyses to test for a structural break when sensitivity of mortgage loan and delinquency growth changes to property price changes considered.

The authors find that two periods are statistically different, while the significance level is lower for Islamic banks. Moreover, loan growth has become less responsive to property price increases; delinquency sensitivity to property price changes has significantly increased in the post-LTV period for conventional banks, while this is not the case for Islamic (participation) banks.

This paper not only increases empirical evidence regarding the effectiveness of LTV ratio policy but also fills the gap in the literature by providing a comparison between conventional banks and Islamic (participation) banks.

This study investigates the relation of bank loan delinquencies to Fed Survey delinquency data from 2003 to 2017. Bank-generated loans have lower delinquencies than all Fed Survey loan types. Survey mortgage and auto loan delinquencies are positively related to bank loan delinquencies indicating complimentary delinquency decisions for borrowers. Conversely, student loans delinquencies are negatively related to bank loans, consistent with borrowers substituting student loan payments for bank debt for the entire sample period. Student loan delinquencies are negatively related to per-capita bankruptcy, and all other types of debt have a positive relation. The relation between Fed Survey loan delinquencies and bank-generated loan delinquencies is time varying and changed after the financial crisis in 2008.

Seemingly Unrelated Regression is used to study delinquencies for three bank loan types and whether or not they are related to Fed Survey loan delinquencies. The sample is split into pre-financial crisis before 2008 and post-crisis after 2008.

Seemingly Unrelated Regression (SUR) results show that bank delinquencies for second mortgages and “Other” loan types are consistently complementary to Fed Survey mortgage loan delinquencies. Fed Survey auto loans delinquencies are also consistent with a complimentary relation, and these results are largely driven by the relation after the financial crisis of 2008 since pre-crisis regression results are not significant for every dependent variable. Credit card loan delinquencies have a negative and substitute relation with bank-generated first mortgage loan delinquencies prior to the crisis in 2008, and with bank-generated second mortgages after the crisis. Conversely, student loan delinquencies from the Fed Survey are negatively and significantly related to bank mortgages for the entire sample period, but only with bank-generated first mortgages after 2008. The student loan delinquency results are consistent with income smoothing, on average, although this is not explicitly tested at the micro level since this study uses macro-level data and not borrower-specific data. These findings are also consistent with conventional wisdom that student loans provide “financial slack” and borrower flexibility.

A limiting factor is this study uses macro-level data and not borrower-specific data.

Empirical findings are consistent with prior research that student loans provide income smoothing and “financial slack,” and borrowers with payment challenges will pay other debt before student loans.

Borrowers in financial trouble tend to be delinquent for all debt, and more so for student debt.

To investigate whether Fed Survey delinquencies of auto loans, first mortgages, student loans and credit card loans from all sources have complementary or substitution effects with bank debt at a macro level. The study investigates whether bank debt follows “market trends” as a complementary effect, or if bank debt has a negative relation to other debt indicating a substitution effect.

The late 2000s Great Recession led to a surge of bank failures in the USA with nearly 300 banks failing from 2009 to 2010. Recalling the farm crises of the 1980s where the farm sector was pinpointed as one of the major precursors of such crises, this study is an attempt to validate if the agricultural sector can once again be considered as a major instigator of the current financial crises.

An early warning model is developed based on factors that may cause bank failures, with special attention given to the role of the agricultural lending portfolios of commercial banks. The model will have several time period versions that will determine the length of time prior to the actual bank bankruptcy declarations that early warning signals could be detected.

The empirical results indicate that credit exposure to the farm sector does not necessarily enhance a bank's tendency to fail or its probability of success or survival. This lends support to the reality that agricultural loan delinquency rates are consistently below the banks' overall loan delinquency rates, thus confirming that agricultural lenders are in relatively stronger financial health. This study instead finds that costly funding arrangements, increasing interest rate risk, and declining asset quality can be possible early warning signals that can be detected as far back as two or three years before eventual bank failure.

This study differentiates itself from previous studies by its special focus on the role of the agricultural finance industry in the ensuing economic crises. This study's early warning model also presents an extended version of previous empirical models as it accounts for measures of capital adequacy, asset quality, management risk, profitability, liquidity risk, loan portfolio composition and risk, funding arrangement, structural and macroeconomic variables.

This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability.

Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach.

Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017.

The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique.

The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans.

The authors show a clear outperformance of the MS model concerning the linear one.

The purpose of this paper is to present a comprehensive framework for assisting lending banks in their current expected credit losses (CECL) forthcoming computations.

The bottom-up approach requires multiple steps including the spline method for identifying optimal segments in the lifetimes of loans, Poisson regressions for evaluating the explanatory variables and hazard rate probes for gaining inferences toward the expected credit losses and their projected schedule.

The CECL paradigm has both advantages and disadvantages, as discussed hereafter.

The model is practical, accurate in the sense that provisions are properly and timely allocated, it can be programmed and it relies on merely a few mild assumptions, thus it can be conveniently calibrated to fit broad macroeconomic scenarios.

This study provides background on the subject, motivate each module, construct the advised model, assemble a pseudo-database, demonstrate the functionality of the procedures and further draw conclusions on the effectiveness of the current strategy.

Using a unique dataset of a commercial microfinance institution (MFI) in Tanzania, the purpose of this paper is to investigate first whether agricultural firms have a different probability to get a loan and whether their loans are differently volume rationed than loans to non‐agricultural firms. Second, the paper analyzes whether agricultural firms repay their loans with different delinquencies than non‐agricultural firms.

The authors estimate a Probit‐Model for the probability of receiving a loan, a Heckman‐Model to investigate the magnitude of volume rationing for all loan applications and an OLS‐Model to examine the loan delinquencies of all microloans disbursed by the MFI.

The results reveal that agricultural firms face higher obstacles to get credit but as soon as they have access to credit, their loans are not differently volume rationed than those of non‐agricultural firms. Furthermore, agricultural firms are less often delinquent when paying back their loans than non‐agricultural firms.

Even if the authors can show that access to credit and loan repayment is different for agricultural firms, the current regional focus of the MFI only allows for lending to agricultural firms in the greater Dar es Salaam area. Thus, these results might change in a rural setting. Besides general differences of the rural economic environment, the production type of agricultural firms might also differ in rural areas. Also, these results might change in different country contexts.

The findings suggest that a higher risk exposition typically attributed to agricultural production must not necessarily lead to higher credit risk. They also show that the investigated MFI overestimates the credit risk of agricultural clients and, hence, should reconsider its risk assessment practice to be able to increase lending to the agricultural sector. In addition, the results might indicate that farmers qualify less often for a loan as they do not fit into the standard microcredit product.

To the authors' knowledge, this is the first paper which simultaneously investigates access to credit and the repayment behavior of agricultural firms.

The purpose of this paper is to identify determinants of feasible outcome events (expired with no loss, settled for loss, still performing) and time to event of Farm Service Agency (FSA) operating and farm ownership (FO) loan guarantees.

Data on 19,126 FSA guaranteed loans, which were made by various lenders to farmers who have limited ability to obtain loans from normal sources without the Federal guarantee, were collected. Cox proportional hazards models for operating loans (OLs) and FO loans are estimated to identify borrower characteristics, loan characteristics, lender types, and farm and macroeconomic environment factors that influence guarantee outcomes.

Loans with different characteristics (loan amount, loan term, lender type, region originated) and assistance programs (Beginning Farmer, Interest Assistance) have differing guarantee outcomes. Contemporaneous variables, in particular delinquency status, have a significant impact on guarantee outcomes.

All loans were originated in calendar years 2004 and 2005. Since FO loans may have as long as 40 year terms, results are not as robust for FO loans as for OLs.

Different loan characteristics and macroeconomic conditions significantly influence the occurrence of possible guarantee outcomes and time to the outcomes.

Guaranteed loans are the primary method of government credit assistance to US farm operators. Data on individual borrowers have been difficult to obtain for much of the life of the guaranteed program because loan applications are held privately. This study provides insight on how various factors drive guarantee performance which is useful to policy makers trying to increase guaranteed loan program efficiency.

The purpose of this paper is to analyze the differences between the actual mortgage prompt and late payments and their respective expected measures from 2004 to 2010 to spot early symptoms of housing crisis.

This paper explores these discrepancies across the entire US market and along various delinquency lengths of 30, 60 and 90 days. This paper constructs a Bayesian forecasting model that relies on prior distributional properties of diverse time horizons.

Abnormal mortgage delinquency rates are identified in real time and can be served as early symptoms for housing crisis.

The statistical scheme proposed in this paper can function as a valuable predictive tool for lending institutions, bank audit companies, regulatory bodies and real estate professional investors who examine changes in economic settings and trends in short sale leads.

The abnormal mortgage delinquencies can serve as indicators of changes in economic fundamentals and early signs of a mounting housing crisis.

This paper presents a unique statistical technique in the context of mortgage delinquencies.

The primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.

Using 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.

In analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.

This study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.